An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph - CrossMinds.ai
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
Aug 13, 20202 views
Jiarui Jin
There is an influx of heterogeneous information network (HIN),based recommender systems in recent years since HIN is capable of characterizing complex graphs and contains rich semantics.,Although the existing approaches have achieved performance improvement, while practical, they still face the following problems.,On one hand, most existing HIN-based methods rely on explicit path,reachability to leverage path-based semantic relatedness between,users and items,,e.g.,, metapath-based similarities. These methods,are hard to use and integrate since path connections are sparse or,noisy, and are often of different lengths. On the other hand, other,graph-based methods aim to learn effective heterogeneous network,representations by compressing node together with its neighborhood information into single embedding before prediction. This,weakly coupled manner in modeling overlooks the rich interactions,among nodes, which introduces an early summarization issue. In,this paper, we propose an end-to-end Neighborhood-based Interaction Model for Recommendation (NIRec) to address above problems.,Specifically, we first analyze the significance of learning interactions in HINs and then propose a novel formulation to capture,the interactive patterns between each pair of nodes through their,metapath-guided neighborhoods. Then, to explore complex interactions between metapaths and deal with the learning complexity,on large-scale networks, we formulate interaction in a convolutional way and learn efficiently with fast Fourier transform. The,extensive experiments on four different types of heterogeneous,graphs demonstrate the performance gains of NIRec comparing,with state-of-the-arts. To the best of our knowledge, this is the first,work providing an efficient neighborhood-based interaction model,in the HIN-based recommendations.
SIGKDD_2020
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